In today’s fiercely competitive restaurant industry, more is needed than simply serving great food and providing excellent service. There is no doubt that listening to the voice of the customer is crucial across all industries, but particularly for restuarants. In fact, Denny’s Canada recently created an entire ad campaign inviting consumers to weigh in on Denny’s menu, utilizing their feedback to potentially help remove some dishes, while making some new items permanent staples.
The restaurant industry is arguably one of the most competitive regarding customer loyalty, especially with the evolution of online ordering and delivery services. And with 92 percent of global consumers saying they don’t consider themselves brand loyal to the restaurant industry, harnessing the power of AI to hear what customers are actually saying will keep them coming back.
To succeed in the digital age, restaurants must use AI-driven insights to help restaurants and marketers understand how customers perceive their brand in each location, identify top customer complaints, celebrate where they’re succeeding, and even predict the consumer’s wants and needs. Moreover, AI enables multi-location brands to compare themselves to local competitors and understand the factors influencing customers’ choices. KFC recently boosted revenue by 13 percent by better understanding their customers.
While AI is revolutionizing the restaurant industry and empowering brands to make data-driven decisions, it’s essential to know what data provides the most actionable insights. Some methods, such as aggregate ranking, can be misleading to marketers. To truly understand each location’s needs, restaurants must look at the granular data.
The Age of Brand Intelligence
By 2025, 80 percent of the world’s data will be unstructured and restaurant brands have the opportunity to tap into this goldmine of unstructured data—data collected from various sources like online reviews, social media, and customer feedback. AI is driving this new level of brand intelligence by quickly and efficiently filtering through the noise of unstructured data and revealing insights like customer sentiment, predictive analysis, and personalized recommendations.
1. Real-time Analysis
AI tools can process vast amounts of data in real time, meaning restaurants can instantly track and analyze customer feedback. Let’s say a diner sits down in a Tulsa, Oklahoma, restaurant and reviews a lousy meal from their table, a manager would have the ability to change the diner’s experience on-site, or a team could respond fast enough to offer a solution, turning that diner’s experience into a better one.
2. Identifying Top Customer Complaints
AI-powered sentiment analysis can pinpoint recurring themes and common complaints in customer reviews. Taco Bell returned their “Taco lovers pass” after customer sentiment analysis quickly showed customers wanted it back. Surfacing this information allows restaurants to act swiftly and identify problem areas: slow service, cleanliness, or menu items. Addressing these issues leads to improved customer experiences and increased brand loyalty.
3. Predictive Insights
Beyond identifying current concerns, AI can predict more significant issues by analyzing historical review data. For instance, if specific keywords like “dark parking lot” or “raw chicken” come up again and again, it can indicate an underlying issue that needs attention before it escalates.
4. Localized Intelligence
AI-driven analysis can provide a detailed understanding of how each restaurant location is performing. Marketers can compare locations, pinpointing which are thriving and which need improvement. This knowledge empowers marketers to create strategies to outshine competitors in their local markets.
Competition in the Local Landscape
Multi-location restaurant brands often find themselves in fierce competition with local eateries. AI provides the necessary tools to measure performance and glean what the local customer prefers.
1. Competitor Analysis
AI allows multi-location brands to monitor and assess the performance of local competitors. This data can reveal areas where a restaurant outperforms its competition and where improvements are necessary. Using AI to display side-by-side comparisons of a brand and the competition across reviews, social media, and local brand visibility—whether looking into a single restaurant or the entire brand – is essential to understanding where the restaurant stands.
2. Influencing Factors
There are a lot of factors that influence a customer’s decisions when choosing a restaurant. AI can help reveal what matters to the customer: price sensitivity, menu, customer service, and/or online presence. Armed with this knowledge, multi-location brands can fine-tune their strategies to better meet customer expectations.
3. Personalization
Multi-location brands have a lot to think about when it comes to personalization. AI solutions simplify the complicated and provide personalized business recommendations by reading, analyzing, and reporting hundreds, thousands, or millions of customer reviews and comments about brands. Whether having different menu items based on the location or utilizing generative AI to respond to reviews with the right brand voice and tone, businesses will gain tremendous efficiency without sacrificing customer satisfaction.
Drilling down into who diners are, what they want in a local restaurant, and how multi-location brands can provide it is at the heart of AI-powered solutions. AI empowers those in the restaurant industry to do this efficiently to compare themselves to local competitors, adapt their strategies to meet customers where they’re at, and drive brand loyalty. In an increasingly competitive market, AI is not just a technological tool; it’s a game-changer that can drive business success and brand excellence.
John Mazur is the CEO of Chatmeter. With an accomplished background in scaling technology companies, John brings decades of experience to the CEO position. He joined Chatmeter from CoStar Group, where he was President of Residential, responsible for its marketplace business since 2020. Previously, John was CEO of Homesnap, which he helped build into a market leader before selling it to CoStar Group for $250M in 2020. John has also held roles as CRO at Belly, a SaaS-based customer loyalty company, and ReachLocal, an online marketing solutions provider, where he was the CEO of the European Division and helped build from start-up to over $100M in revenue, eventually going public in 2010. John holds an MBA from Columbia Business School.
